Atlas and methods for segmentation and alignment of anatomical data

ABSTRACT

The present invention provides an atlas comprising values representative of magnetic resonance properties of a magnetic resonance (MR) scan and optionally, prior probability data relating to tissue type. Further embodiments of the invention involve a system including an MR scanner and the atlas for use in alignment of an MR scan, such as a localizer scan, to obtain a specific geometry of the data acquired during a subsequent scan. Also, a system includes an MR scanner and the atlas for automatic segmentation of an MR scan. Methods of making and using the atlas and system are also provided.

TECHNICAL FIELD

The present invention generally relates to magnetic resonance and otherbiological scan data.

BACKGROUND

Magnetic resonance imaging is a complex interaction between protons inbiological tissues, a static and alternating magnetic field (themagnet), and energy in the form of radio-frequency waves of a specificfrequency (RF), introduced by coils placed next to the subject. Theenergy state of the hydrogen protons is transiently increased. Thesubsequent return to equilibrium (relaxation) of the protons results inthe release of RF energy which can be measured by the same surface coilsthat delivered the RF pulses. The RF energy, also referred to as the RFsignal or echo, is complex and is thus transformed by Fourier analysisinto useful information used to form an MR image.

SUMMARY

The present invention provides apparatus and methods for processing dataassociated with magnetic resonance (MR) scanning. In particular, in oneembodiment, the present invention provides an atlas comprising at leastone value representative of a magnetic property and, optionally, atleast one value representative of tissue type prior probability. In afurther embodiment, the present invention provides an atlas comprising aplurality of values representative of magnetic properties of a pluralityof spatial locations of a plurality of subjects. In one embodiment, asystem is provided having both an MR scanner and an atlas of the presentinvention. In a further embodiment, the invention provides methods ofmaking and using the atlas and system.

The apparatus and methods of the present invention provide a modelhaving data representative of one or more subjects. The data includesmagnetic property values, optionally, tissue type prior probabilityvalues. The atlas can be used to automatically align an MR scan, such asa localizer scan, to obtain a specific geometry of the data acquiredduring a subsequent scan. The atlas may also be used to automaticallyidentify, or segment, tissue type of a subject based on MR scan data ofthe subject.

According to one embodiment of the invention, an atlas is providedcomprising a plurality of values representative of a magnetic propertyof a plurality of spatial locations of a subject as determined bymagnetic resonance. According to a further embodiment, an atlas isprovided comprising values representative of a statisticalrepresentation of a magnetic property of a plurality of spatiallocations of a plurality of subjects. The present invention alsoprovides a system comprising an MR scanner and an atlas. For example,the atlas may contain magnetic property data. The system can be used toautomatically align an MR scan, such as a localizer scan, to obtain aspecific orientation of the data acquired during a subsequent scan. Thesystem may also be used to automatically identify, or segment, tissuetype of a subject based on MR scan data of the subject.

Methods of using the atlas are further provided herein. In oneembodiment, a method of using the atlas having magnetic property valuesto obtain a specific geometry of data to be acquired during a subsequentscan is provided. In a variation of this embodiment, a method of usingthe atlas may additionally involve tissue type probabilities.

Methods of using the atlas are further provided herein. In oneembodiment, a method of using the atlas having magnetic property valuesto determine tissue type is provided. In a variation of this embodiment,a method of using the atlas may additionally involve tissue typeprobabilities.

According to a further embodiment of the invention, a method is providedfor obtaining information about a subject having the steps of providinga magnetic resonance scanner, providing an atlas having magneticresonance data derived from at least one other subject and processinginformation received from the scanner pertaining to the subject. Alsoincluded are the steps of reading the atlas and determining alignment ofthe magnetic resonance scan to obtain a specific geometry of asubsequent magnetic resonance scan.

According to another embodiment of the invention, another method isprovided for obtaining information about a subject. This method involvesthe steps of providing magnetic property values corresponding to tissuetypes pertaining to the subject, providing an atlas having magneticproperty values derived from at least one other subject, along withlabeling tissue types of a tissue corresponding to the magneticresonance property values pertaining to the subject by using the atlashaving the magnetic resonance values derived from at least one othersubject.

According to a further embodiment of the invention, a method is providedfor creating an atlas by providing a first magnetic resonance modalityvolume pertaining to a subject, divided into voxels, and recording amagnetic property value in a node of the atlas corresponding to a voxelof the first magnetic resonance modality volume.

Another embodiment of the invention involves a method for creating anatlas. A first magnetic resonance modality volume is provided pertainingto a subject and divided into voxels. A labeled volume is providedindicating tissue types of tissue corresponding to the voxels.Distortion of the first magnetic resonance modality volume is corrected.Magnetic property distribution parameters are extracted for each tissuetype identified at each voxel. Also, magnetic property data is recordedcorresponding to each tissue type in a node of the atlas correspondingto a voxel of the first magnetic resonance modality volume.

According to another embodiment, a method for creating an atlas isprovided wherein a voxel intensity is obtained from an imagerepresentative of at least one magnetic modality of a voxel of asubject, a magnetic property value is derived from the voxel intensity,and the magnetic property value is written to a node of the atlascorresponding to the voxel.

A further embodiment of the invention provides a method for processingan image of a subject. An atlas is provided having magnetic propertyvalues derived from at least one other subject. The image is aligned tothe atlas, and the image is segmented into segments. The segments arelabeled to designate a tissue type of a tissue corresponding to themagnetic property values pertaining to the subject by the use of theatlas. An image is thus obtained pertaining to magnetic property valuesof the subject.

It will further be appreciated that in the methods of the presentinvention, distortion may be corrected prior to entering the data intothe atlas, as well as prior to processing newly acquired data inconjunction with the atlas.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be apparent from the description herein and theaccompanying drawings, in which like reference characters refer to thesame parts throughout the different views.

FIG. 1 provides a subject and a grid pattern illustrating voxels of asubject;

FIG. 2 illustrates an atlas;

FIGS. 3-8 illustrate nodes of an atlas according to various embodimentsof the invention.;

FIGS. 9A and 9B illustrate sample data for determination of the contentof a node of an atlas;

FIG. 10 provides a sample method for the creation of an atlas;

FIG. 11 provides a sample method for the registration of MR data to anatlas; and

FIG. 12 provides a functional schematic of a system according to anembodiment of the invention.

DETAILED DESCRIPTION

The present invention, in various embodiments, involves an atlascontaining values representative of magnetic properties of a magneticresonance (MR) scan and optionally prior probability data relating totissue type. Further embodiments of the invention involve a systemincluding an MR scanner and the atlas for use, for example, in alignmentof an MR scan and for automatic segmentation of an MR scan. Methods ofcreating and using the atlas and system are also provided.

As used herein, the following terms are defined as follows:

T1 and T2 relaxation times: The rate of return to equilibrium ofperturbed protons is referred to as the relaxation rate. The relaxationrate is different for different normal and pathologic tissues. Therelaxation rate of a hydrogen proton in a tissue is influenced bysurrounding molecular environment and atomic neighbors. Two relaxationrates, the T1 and T2 relaxation times, may be measured. The T1relaxation rate is the time for 63% of the protons to return to theirnormal equilibrium state, while the T2 relaxation rate is the time for63% of the protons to become dephased owing to interactions amongadjacent protons. The intensity of the signal and thus the imagecontrast can be modulated by altering certain parameters, such as theinterval between RF pulses (TR) and the time between the RF pulse andthe signal reception (TE). So-called T1-weighted (T1W) images areproduced by keeping the TR and TE relatively short. Under theseconditions, contrast between structures is based primarily on their T1relaxation differences. T2-weighted (T2W) images are produced by usinglonger TR and TE times.

TR: The time between repetitions of RF in an imaging sequence.

TE: The time between the RF pulse and the maximum in the echo in aspin-echo sequence.

Flip Angle: The angle that the magnetic moment vector rotates whenapplying a B1 RF pulse field.

T1: The time to reduce the difference between the longitudinalmagnetization and its equilibrium magnetization by an exponentialfactor.

T2: The time to reduce the transverse magnetization by an exponentialfactor.

PD: The concentration of spins.

T1-weighted: A magnetic resonance image where the contrast ispredominantly dependent on T1.

T2-weighted: A magnetic resonance image where the contrast ispredominantly dependent on T2.

PD-weighted: A magnetic resonance image where the contrast ispredominantly dependent on PD.

Diffusion-weighted: A magnetic resonance image where the contrast ispredominantly dependent on diffusion weighting gradient.

Magnetization Transfer-weighted: A magnetic resonance image where thecontrast is predominantly dependent on magnetization transfer saturationeffect.

Tissue Type: As used herein, “tissue type” can be used to designate aclassification or characteristic of a tissue, such as tissue within avoxel. For example, when used with a human brain as the subject, tissuetype can include, without limitation, gray matter, white matter andcerebral spinal fluid. Optionally, the tissue type can be more specific,such as referring to anatomical structure. For example, in the case of abrain as the subject, the tissue type may designate gray matter and/or,more specifically, hippocampus, or other appropriate anatomicalstructure label. In another example, in the case of a spine as asubject, the tissue type may designate bone, and/or more specificallyvertebral bodies, or other appropriate anatomical structure labels. Inyet another example, in the case of the kidney as a subject, the tissuetype may designate the cortex, and/or more specifically nephrons, orother appropriate anatomical structure labels.

Localizer scan: A low-resolution scan acquired at the beginning of ascanning procedure to estimate the precision of the acquisition geometryrelative to the subject to be imaged.

Subsequent scan: A high-resolution scan acquired on the basis of thelocalizer geometry, such as orientation, dimensions, or voxel size.

Magnetic property: A magnetic property of protons, such as T2, T1, PD,diffusion or magnetization transfer.

The present invention is applicable to a wide variety of MR scans of asubject including mammals (e.g. humans), as well as specific portions ofa subject (e.g., organ, limb, or a portion of an organ or limb), alsoreferred to herein as the “subject”. Each subject is divided inthree-dimensional space into voxels. With reference to FIG. 1, a subject100, such as a human brain, is shown with an illustrative grid pattern200 signifying the locations of voxels 210. Each voxel 210 represents athree-dimensional portion of the subject 100. A voxel 210 may be ofvarious dimensions and can have different dimensions along differentaxes within the subject 100.

As shown in FIG. 2, an atlas 300 is provided according to an embodimentof the invention. While illustrated as a three-dimensional structure,the invention is not so limited, as the atlas 300 may be formed of anyof a variety of data structures as will be apparent to one of ordinaryskill in the art. The atlas 300 includes nodes 310. According to anembodiment of the invention, each node 310 corresponds to a voxel 210(of. FIG. 1) representing a portion of the subject 100. Alternatives ofthe invention may involve fewer nodes 310 than voxels 210. In such acase, a node 310 may be configured to represent a plurality of voxels210 or the nodes 310 may represent only a subset of the overall voxels210.

FIGS. 3-8 provide various configurations of the nodes 310 according toalternative embodiments of the invention. Each node 310 is configured tostore information relating to the corresponding voxel 210. As shown inFIG. 3, the node 310 may be configured to have a magnetic property 320corresponding to the voxel 210. magnetic properties may include, but arenot limited to, T1, T2, proton density (PD), T2*, magnetizationtransfer, diffusion tensor and derived variables, such as anisotropy anddiffusivity. According to one embodiment of the invention, the magneticproperties may be computed from the images, based on a forward model,and the MR acquisition parameters, including, but not limited to, TR,TE, and flip angle. Determination of such magnetic properties anddetails regarding the MR acquisition parameters can be found in MagneticResonance Imaging, Physical Principle and Sequence Design, E. M. Haackeet al., Wiley-Liss, 1999, pp. 637-667, which is incorporated herein byreference.

Optionally, a second magnetic property 330 corresponding to the voxel210 may also be stored in the node 310. Additional magnetic propertiesmay also be stored in the node 310.

A tissue type prior probability 340 corresponding to a tissue type foundin the voxel 210 may optionally be stored in the node 310. When usedwith a human brain as the subject, tissue type can include, withoutlimitation, gray matter, white matter and cerebral spinal fluid.Optionally, the tissue type can be more specific, such as referring toanatomical structure. For example, in the case of a brain as thesubject, the tissue type may designate gray matter and/or, morespecifically, the hippocampus, or other appropriate anatomical structurelabel. In another example, in the case of the spine as a subject, thetissue type may designate bone, and/or more specifically vertebralbodies, or other appropriate anatomical structure labels. In yet anotherexample, in the case of the kidney as a subject, the tissue type maydesignate the cortex, and/or more specifically nephrons, or otherappropriate anatomical structure labels. It will be appreciated that thetissue type of the voxel 210 may be determined by human labeling or maybe determined by other known methods such as an algorithm (e.g. AdaptiveSegmentation of MRI Data, Wells W M, at al., IEEE Transactions onMedical Imaging, 1996;15:429-442 (corrected version available at

http://citeseer.nj.nec.com/cache/papers/cs/19782/http:zSzzSzsplweb.bwh.harvard.edu:8000zSzpageszSzpplzSzswzSzpaperszSztmi-96.pdf/wells96adaptive.pdf),Statistical Approach to Segmentation of Single-Channel Cerebral MRImages, Rajapakse J C, et al., IEEE Transactions on Medical Imaging,1997, Vol. 16, No.2: 176-86, and Automated Model-Based Bias FieldCorrection of MR Images of the Brain, Van Leemput, K. et al., IEEETransactions on Medical Imaging, 1999, Vol. 18, No. 10), which areincorporated herein by reference.

According to a further embodiment of the invention, a node 310 mayinclude a tissue type prior probability 340 corresponding to a tissuetype found in the voxel 210, as illustrated in FIG. 4. According to thisembodiment, a first magnetic property 320 is also stored. Optionally, asecond magnetic property 330, or additional magnetic properties, mayalso be stored in the node 310.

According to a further embodiment of the invention, as shown in FIG. 5,one or multiple magnetic properties may be determined for each of thetissue types located at the corresponding voxel 210. Therefore, as shownby way of example in FIG. 5, if a voxel 210 has two tissue types locatedat the voxel 210, as determined from a plurality of subjects, one ormore magnetic properties 320, 330 may be stored for each of the tissuetypes. As shown in FIG. 5, a value of a first magnetic property 320 maybe stored for the tissue type 1 at the corresponding voxel. Optionally,a value of a second magnetic property 330 may also be stored for tissuetype 1. Separate magnetic properties 320, 330 may also be stored for thevalues corresponding to the tissue of tissue type 2. This variation ofthe invention is useful in conjunction with an atlas 300 formed ofinformation from more than one subject 100. A tissue type probability340 may also be optionally stored in the node 310 for one or more of thetissue types detected at the corresponding voxel 210.

In a further embodiment, illustrated by way of example in FIG. 6, atissue type prior probability 340 may be stored at a node 310 for eachtissue type located at a corresponding voxel 210. A magnetic property320 is also stored at the node 310 for each tissue type. Optionally, oneor more further magnetic properties 330 may also be stored at the node310.

A further embodiment of a node 310 is illustrated in FIG. 7. The node310 of FIG. 7 provides a tissue type prior probability 340 andstatistical data pertaining to a magnetic property of the tissue of acorresponding voxel 210, relative to a plurality of subjects. As shownby way of example in FIG. 7, a mean 322 of the values of a firstmagnetic property for a first tissue type at the corresponding voxel 210is provided. A variance 324 of the values of a first magnetic propertyfor the first tissue type at the corresponding voxel 210 is alsoprovided.

The node 310 of FIG. 7 may also optionally include statistical datapertaining to one or more additional magnetic properties, such as a mean332 and variance 334 of a second magnetic property.

The node 310 of FIG. 7 is also optionally suitable for use with an atlas300 containing information from a plurality of subjects 100. Any of thedata 322, 324, 332, 334, 340 as described above in relation to a firsttissue type, may also be determined in relation to a second tissue typeand stored.

FIG. 8 illustrates a node 310 of a further embodiment of the inventionproviding statistical data, such as a mean 322 and a variance 324, ofthe values of a first magnetic property for a first tissue type at acorresponding voxel 210. Optionally, further statistical data 332, 334or a tissue type prior probability 340 may be provided. Similarinformation 322, 324, 332, 334, 340 may also be optionally providedrelating to further tissue types at a corresponding voxel 210.

As illustrated by way of example in FIGS. 9A and 9B, the determinationof a mean 322 and a variance 324 for a first magnetic property can bedetermined. FIG. 9A provides a table 400 having the sample magneticproperty values for an analogous voxel of each of three subjects. FIG.9B illustrates the three steps 410, 420, 430 involved in determining thecontent of the node 310 corresponding to the illustrative voxel. Asshown in step 1,410, the tissue type is 1, the mean of the value is 100and the variance is 0. The prior probability of this node correspondingto a voxel of tissue type 1 is 1. Step 2,420, adds the data of thesecond subject to the data already tabulated from the first subject.Therefore, the mean now rises to 150, while the remaining data isunchanged, as the tissue type is 1 for both subjects, leaving the priorprobability at 1.

Step 3,430, illustrates a node configuration illustrated in FIG. 7 or 8by the tabulation of statistical data per tissue type for each node.Because the tissue type for the third subject is 2, a second set ofstatistical data is tabulated for the new tissue type, while the firstset of data is updated in view of the third subject. The mean andvariance of tissue type 1 remain unchanged. The prior probability oftissue type 1, however, now changes to ⅔. The mean of tissue type 2 is50, and the variance 0. The prior probability of tissue type 2 is ⅓.

In another embodiment, additional data may be stored at each noderelating to the corresponding voxel or a representation thereof. Forexample, image intensity data, expressed in arbitrary units, may bestored. Alternatives include those apparent to one of skill in the art.

In another embodiment, global prior probabilities may be stored in theatlas of the present invention. Global probabilities indicate theoverall prior probability of something, such as a tissue type appearingin a particular area, or anywhere, in a subject. The global mean andvariance of various magnetic properties may also be determined andstored for each tissue type. Such global values may be stored at avariety of locations in the atlas, such as in a header, oralternatively, at each node.

As shown by way of example in FIG. 10, a method 500 is providedaccording to an embodiment of the invention for the creation of an atlas300. The atlas is built from one or more subject data sets 510, 512,514. A subject data set may contain at least one MR scan 516, 518, 520of a subject (e.g. an organ or a portion of an organ). The MR scans canbe, but are not limited to, T1, T2, proton density (PD), T2*,magnetization transfer, diffusion tensor or derived variables such asanisotropy and diffusivity.

Distortions are then corrected in the MR scan 516, step 530. Correctionsof distortion are known to one of ordinary skill in the art and arediscussed in more detail in relation to FIG. 11 herein.

According to one embodiment, a subject's data set used in creating oradding to an atlas can also contain a labeled representation 522, 524,526 of the MR scan(s), such as a segmented volume identifying eachtissue type/anatomical structure. The labeled representation can beobtained by way of manual labeling (e.g. by experienced anatomists)and/or by way of automatic segmentation methods as described by way ofexample in Wells, supra, Statistical Approach to Segmentation ofSingle-Channel Cerebral MR Images, Rajapakse J C, et al., IEEETransactions on Medical Imaging, 1997, Vol. 16, No.2: 176-86, andAutomated Model-Based Bias Field Correction of MR Images of the Brain,Van Leemput, K. et al., IEEE Transactions on Medical Imaging, 1999, Vol.18, No. 10, which are incorporated herein by reference.

Next, the tissue type and corresponding magnetic property statisticaldistribution data is extracted from the corrected subject data set 510,step 540.

A high-resolution temporary atlas 560, step 550, is then created bystoring the tissue type and corresponding magnetic property statisticaldata of each voxel 210 of the subject, in each corresponding node 310 ofthe atlas 300.

The high-resolution temporary atlas 560 may then be used as the atlas300 if the atlas 300 is to only have data pertaining to a singlesubject.

However, if additional subjects are to be added, the method 500continues with the subject data set 512 of a second subject, and,optionally subject data sets 514 of additional subjects. Correction ofdistortion, step 530, and extraction of statistical data 540 isconducted as in relation to the first subject data set 510.

After each additional subject data set 512, 514 is processed, the tissuetype and corresponding magnetic property statistical data of each voxel210 of the subject is registered, or aligned, with the existing nodestructure of the atlas 300, step 570. During registration, the data,such as tissue type and magnetic statistical data, corresponding to thevoxels 210 of the subject, is manipulated to correspond to the analogousvoxels 210 represented by the node 310 structure of the atlas. Furtherdetails of registration, step 570, are discussed in detail in relationto FIG. 11 herein.

Next, the additional data, such as tissue type and magnetic statisticaldata, is then added to the atlas 300 by updating the atlas parameters,step 580. As shown in FIG. 10, a high-resolution atlas 565 is producedafter the addition of two subject data sets 510, 512 to the atlas 300.This high-resolution atlas 565 may be used as an atlas 300, oradditional subject data sets 514 may be added.

When the desired N subject data sets have been added to the atlas, theatlas may optionally be subsampled, step 590 to create the atlas 300. Asdiscussed herein, alternatives of the invention may involve fewer nodes310 than voxels 210. In such a case, a node 310 may be configured torepresent a plurality of voxels 210 or the nodes 310 may represent onlya subset of the overall voxels 210. Such a reduced resolution may alsobe generated by the subsampling, step 590, by combining data frommultiple voxels into one node. Also, only a portion of the voxelsrepresenting a portion of the subject may be used in the atlas 300.

An atlas 300 of the present invention may be customized for aspecialized purpose. The atlas may have values of a statisticalrepresentation that are population-specific (e.g., related to age, sexand/or pathology), scanner-specific (e.g., related to manufacturerand/or scanner model), and/or acquisition sequence-specific (e.g.,related to flash and/or inversion recovery). Acquisition sequences caninclude including, without limitation, at least one from the group ofPD-, T2-, T1-, diffusion-, and magnetization transfer-weighted.Acquisition sequence-specific values may involve magnetic resonancesequence parameters, including, without limitation, at least one fromthe group of TR, TE and flip angle.

An atlas of the present invention may be oriented to various coordinatesystems. One such example of a coordinate system is a Cartesiancoordinate system, such as a Right Anterior Superior (RAS) coordinatesystem, used in orienting an image relative to a subject, or anarbitrarily determined coordinate system.

An atlas of the present invention may be created at various spatialresolutions. An atlas may further be sub-sampled to reduce theresolution and data required and time required for calculations. Theresolution may also vary within an atlas, allowing greater resolution atareas of interest.

According to one embodiment of the invention, an atlas may beconstructed as shown in FIGS. 10 and 11. Optionally, an atlas may beformed by data from only one subject. An atlas may be formed by Nsubjects, which may be determined by monitoring the change of valueswithin the atlas upon the addition of each additional subject. Accordingto another embodiment, when the values stored in the nodes of the atlasno longer vary within a statistical range of confidence, the addition offurther subjects is no longer required.

The registration of data onto the atlas may comprise the determinationof at least 6 parameters. For example, those parameters can be 3translation shifts, 3 scaling factors and 3 rotation angles relativelyto the 3 orthogonal directions of the atlas coordinate system.

Further detail regarding registration of data onto an atlas, ortemporary atlas as described in FIG. 10, is illustrated by way ofexample in the method 700 of FIG. 11. In FIG. 11, a method 700 isprovided according to an embodiment of the invention for theregistration of MR data to an atlas 300. The example method 700 of FIG.11 is also applicable to prior probability data or any other data typesfor association to nodes 310 of the atlas 300.

An initial set of registration parameters is provided, step 710, alongwith an initial bias estimate, step 720, according to methods known toone of skill in the art in relation to atlases having other types ofdata. See, for example, Wells, supra, Automated Model-Based Bias FieldCorrection of MR Images of the Brain, Van Leemput, K. et al., IEEETransactions on Medical Imaging, 1999, Vol. 18, No. 10, and AutomaticScan Prescription for Brain MRI, Itti, L. et al., Magnetic Resonance inMedicine, 2001, Vol. 45: 486-494, which are incorporated herein byreference. The initial bias estimate of step 720 adjusts for intensityfall-off in the portions of the image away from the image center.

A magnetic resonance (MR) volume is also provided, step 730. Themagnetic resonance volume can be generated by deriving a magneticproperty value for a voxel from a voxel intensity value of acorresponding voxel of an image containing magnetic resonance data.

A bias correction is applied to the MR volume, step 740. With regard tostep 740, and step 530 of FIG. 10, distortion and bias can be caused bya variety of factors. For example, the distortion and bias can besubject-dependent, such as from, but not limited to, chemical shift,magnetic susceptibility, and/or per-acquisition motion. Alternatively orin addition, distortion and bias can be scanner-dependent, such as from,but not limited to, gradients non-linearity, main magnetic fieldnon-homogeneity and/or eddy currents. Maxwell effects are a furthersource of potential distortion or bias.

Correction of such distortion and bias are known to one of ordinaryskill in the art.

As shown in FIGS. 10 and 11, bias and distortion are corrected prior toincorporating the data into the atlas. According to a further embodimentof the invention, distortion and bias are corrected prior to processingdata in conjunction with the atlas.

A transform is applied to the bias-corrected MR volume, step 750. Lineartransformations (e.g. translation, rotation, scaling) are applied toimages via homogeneous matrices. According to one embodiment, they are4×4 matrices, wherein the 3 first bottom elements always equal 0 and thelast bottom elements always equals 1. Any transformation can bedecomposed into a translation, a rotation and a scaling matrices. Thefinal homogeneous matrix is then a multiplication of those 3 matrices.Details are given by way of example below:

-   -   tx, ty and tz being the translation parameters in the x, y and z        directions, the translation homogeneous matrix is given by:

${\begin{pmatrix}1 & 0 & 0 & t_{x} \\0 & 1 & 0 & t_{y} \\0 & 0 & 1 & t_{z} \\0 & 0 & 0 & 1\end{pmatrix}\quad}\quad$

-   -   xs, ys and zs being the scaling parameters in the x, y and z        directions, the scaling homogeneous matrix is given by:

$\begin{pmatrix}x_{s} & 0 & 0 & 0 \\0 & y_{s} & 0 & 0 \\0 & 0 & z_{s} & 0 \\0 & 0 & 0 & 1\end{pmatrix}\quad$

θ, φ and φ being the rotation parameters relatively to the x, y and zaxis, the rotation homogeneous matrix is given by:

$\begin{pmatrix}{{\cos\;{\varphi cos}\;\phi} + {\sin\;{\varphi sin}\;{\theta sin}\;\phi}} & {{\sin\;{\varphi cos}\;\theta} - {\cos\;{\varphi sin}\;{\theta sin}\;\phi}} & {\cos\;{\theta sin}\;\varphi} & 0 \\{{- \sin}\;{\varphi cos}\;\theta} & {\cos\;{\varphi cos}\;\theta} & {\sin\;\theta} & 0 \\{{\sin\;{\varphi sin}\;{\theta cos}\;\phi} - {\cos\;{\varphi sin}\;\theta}} & {{{- \cos}\;{\varphi sin}\;\theta} - {\sin\;{\varphi sin}\;\theta}} & {\cos\;{\theta cos}\;\varphi} & 0 \\0 & 0 & 0 & 1\end{pmatrix}\quad$

The voxels, or MRI points, corresponding to nodes 310 of the atlas 300are segmented based on a Maximum A Posteriori (MAP) estimator, step 760.The MAP estimator is a probability computation with statisticalinformation stored in the atlas. The MAP estimator and its use withother types of data are known to one of ordinary skill in the art, asillustrated by way of example in Wells, supra, Statistical Approach toSegmentation of Single-Channel Cerebral MR Images, Rajapakse J C, etal., IEEE Transactions on Medical Imaging, 1997, Vol. 16, No.2: 176-86,and Automated Model-Based Bias Field Correction of MR Images of theBrain, Van Leemput, K. et al., IEEE Transactions on Medical Imaging,1999, Vol. 18, No. 10, which are incorporated herein by reference.

The registration parameters and bias estimation are then updated, step770, as is known to one of ordinary skill in the art, as illustrated byway of example in Wells, supra, Automated Model-Based Bias FieldCorrection of MR Images of the Brain, Van Leemput, K. et al., IEEETransactions on Medical Imaging, 1999, Vol. 18, No. 10, andMultimodality Image Registration by maximization of Mutual Information,Maes, F. et al., IEEE Transactions on Medical Imaging, 1997, Vol. 16,No. 2, which are incorporated herein by reference. If the target MAP isnot reached, the process repeats, step 780, beginning again withapplication of bias correction to the MR volume, step 740.

If the target MAP is reached, the registration matrix is provided, step790. The registration matrix can include sixteen (16) values, includingtranslation parameters, scaling parameters, and a combination of thesines and cosines of rotation parameters. The registration matrix can beused to obtain a specific geometry (e.g. orientation and/or dimensions)of the data acquired during a subsequent scan, as discussed herein.

The MR volume corrected for bias is also provided, step 800, allowingmore accurate computation of the magnetic property values for each nodeof the atlas.

Further information regarding the details of the steps of FIG. 11 can befound in Wells, supra, Automated Model-Based Bias Field Correction of MRImages of the Brain, Van Leemput, K. et al., IEEE Transactions onMedical Imaging, 1999, Vol. 18, No. 10, and Multimodality ImageRegistration by maximization of Mutual Information, Maes, F. et al.,IEEE Transactions on Medical Imaging, 1997, Vol. 16, No. 2, which areincorporated herein by reference.

According to a further embodiment of the invention, a system 900 isprovided as shown by way of example in FIG. 12. A scanner 910 isprovided to capture magnetic images. A processor 920 is provided tointerface with the scanner 910 and the atlas 300 in order to conduct themethods according to various embodiments of the present invention.

The atlas and system 900 of the present invention may be used in avariety of applications. In one embodiment, a method of using the atlaswith magnetic property data and optionally with tissue (or anatomicalstructure) type prior probabilities is provided, automatically align anMR scan, such as a localizer scan, to obtain a specific geometry of thedata acquired during a subsequent scan (auto-slice prescription).Further details of this implementation can be found in U.S. Pat. No.6,195,409, issued Feb. 27, 2001, to Chang et al., which is incorporatedherein by reference.

In an additional embodiment, a method of using the atlas with magneticproperty data to determine anatomical structure or detect abnormaltissue (auto-segmentation) is provided. Further details of thisimplementation can be found in Wells, supra, Statistical Approach toSegmentation of Single-Channel Cerebral MR Images, Rajapakse J C, etal., IEEE Transactions on Medical Imaging, 1997, Vol. 16, No.2: 176-86,and Automated Model-Based Bias Field Correction of MR Images of theBrain, Van Leemput, K. et al., IEEE Transactions on Medical Imaging,1999, Vol. 18, No. 10, which are incorporated herein by reference.

It will further be appreciated that in the methods of the presentinvention, including the applications described herein, distortion ofnewly obtained data may optionally be corrected prior to processing datain conjunction with the atlas. Further details of distortion correctioncan be found in Sources of Distortion in Functional MRI Data, Jezzard,P. et al., Human Brain Mapping, 1999, Vol. 8:80-85, which isincorporated herein by reference.

The present invention has been described by way of example, andmodifications and variations of the described embodiments will suggestthemselves to skilled artisans in this field without departing from thespirit of the invention. Aspects and characteristics of theabove-described embodiments may be used in combination. The describedembodiments are merely illustrative and should not be consideredrestrictive in any way. The scope of the invention is to be measured bythe appended claims, rather than the preceding description, and allvariations and equivalents that fall within the range of the claims areintended to be embraced therein. The contents of all references,databases, patents and published patent applications cited throughoutthis application are expressly incorporated herein by reference.

1. A method for obtaining information about a subject, comprising thesteps of: providing a prior probability MRI based atlas having magneticresonance data including tissue type prior probability derived from atleast one other subject; processing with a processor informationreceived from a magnetic resonance scanner pertaining to said subject,wherein said information includes data from a magnetic resonance scan;reading said atlas; and determining alignment of said magnetic resonancescan to obtain a specific geometry of a subsequent magnetic resonancescan.
 2. The method of claim 1, further comprising the steps of:communicating alignment data from said processor to said scanner; andautomatically aligning said magnetic resonance scan to obtain saidspecific geometry of a subsequent magnetic resonance scan by the use ofsaid alignment data.
 3. The method of claim 1, wherein said step ofproviding a prior probability MRI based atlas having magnetic propertyvalues derived from at least one other subject involves data derivedfrom a plurality of subjects.
 4. A method of using a processor forobtaining information about a subject, comprising the steps of:providing magnetic property values from a magnetic resonance scannerpertaining to said subject; providing a prior probability MRI basedatlas having at least two magnetic property values for at least onecorresponding voxel derived from at least one other subject; andlabeling by said processor of tissue types of a tissue corresponding tosaid magnetic resonance property values pertaining to said subject bythe use of said atlas having said magnetic resonance values derived fromat least one other subject.
 5. The method of claim 4, wherein said stepof providing a prior probability MRI based atlas having magneticproperty values derived from at least one other subject involves dataderived from a plurality of other subjects.
 6. A method for creating ana prior probability MRI based atlas, comprising the steps of: providinga first magnetic resonance modality volume pertaining to a subject anddivided into voxels; correcting distortion of said first magneticresonance modality volume; recording a magnetic property value in a nodeof said atlas corresponding to a voxel of said first magnetic modalityvolume; providing a second magnetic resonance modality volume pertainingto a second subject and divided into voxels; correcting distortion ofsaid second magnetic resonance modality volume; and updating saidmagnetic property data in said node of said atlas corresponding to avoxel of said second magnetic resonance modality volume.
 7. The methodof claim 6, wherein the step of correcting involves the correction ofdistortion caused by at least one of the group consisting of chemicalshift, magnetic susceptibility, per-acquisition motion, gradientsnon-linearity, main magnetic field non-homogeneity, eddy currents, andMaxwell effects.
 8. The method of claim 6, further comprising the stepsof: providing a plurality of magnetic resonance modality volumespertaining to a plurality of subjects and each of said plurality ofmagnetic resonance modality volumes divided into voxels; correctingdistortion of each of said plurality of magnetic resonance modalityvolumes; and updating said magnetic property value in said node of saidatlas corresponding to a voxel of each of said plurality of magneticresonance modality volumes; wherein said magnetic property value in saidnode of said atlas includes statistical data.
 9. A method for creating aprior probability MRI based atlas, comprising the steps of: providing afirst magnetic resonance modality volume pertaining to a subject anddivided into voxels; providing a labeled volume indicating tissue typesof tissue corresponding to said voxels; correcting distortion of saidfirst magnetic resonance modality volume; extracting magnetic propertydistribution parameters for each tissue type identified at each voxel;recording magnetic property data corresponding to each tissue type in anode of said atlas corresponding to a voxel of said first magneticresonance modality volume; providing a plurality of magnetic resonancemodality volumes pertaining to a plurality of subjects and each of saidplurality of magnetic resonance modality volumes divided into voxels:providing a plurality of labeled volumes corresponding to said pluralityof magnetic resonance modality volumes and indicating tissue types oftissue corresponding to said voxels; correcting distortion of each ofsaid plurality of magnetic resonance modality volume; and updating saidmagnetic property data in said node of said atlas corresponding to avoxel of each of said plurality of magnetic resonance modality volumes.10. A method for obtaining information about a subject, comprising thesteps of: providing a prior probability MRI based atlas having magneticresonance data including more than one magnetic property value priorprobability derived from at least one other subject; processing with aprocessor information received from a magnetic resonance scannerpertaining to said subject, wherein said information includes data froma magnetiC resonance scan; reading said atlas; and determining alignmentof said magnetic resonance scan to obtain a specific geometry of asubsequent magnetic resonance scan.
 11. The method of claim 10 furthercomprising the steps of: communicating alignment data from saidprocessor to said scanner; and automatically aligning said magneticresonance scan to obtain said specific geometry of a subsequent magneticresonance scan by the use of said alignment data.
 12. The method ofclaim 10, wherein said step of providing a prior probability MRI basedatlas having magnetic property values derived from at least one othersubject involves data derived from a plurality of subjects.
 13. A methodof aligning and/or segmenting an MR scan, the method comprising:scanning a subject with an MR scanner to produce a magnetic resonanceimage; interfacing the magnetic resonance image with a prior probabilityMRI based atlas, which includes nodes which each include: tissue typeprobability information, magnetic property values, and locationinformation to determine, from the scan, the probability of tissue typeby location in the magnetic resonance image; and aligning the MR scanand/or segmenting the MR scan.
 14. A method of aligning and/orsegmenting an MR scan, the method comprising: providing a priorprobability MRI based atlas from at least three MR scans of othersubjects which define node locations in anatomic atlas space which areindependent of MR imaging platform voxel properties whereas voxels aredependent upon MR imaging instrument instructions and nodes areindependent of MR imaging platform instructions, each node includingprobability information for at least four independent properties:location in three dimensional atomic atlas space, prior probability ofspecific tissue types at this nodal location, statistics of at least onemagnetic resonance property assigned to specific tissue types at thisnodal location, and prior probability of specific tissue types ofneighboring nodes for each tissue type at this nodal location, whereinmagnetic resonance property values include one or more parameters suchas T₁ or T₂ for the identical nodal location in anatomic space; scanninga subject with an MR scanner to produce a magnetic resonance imagerepresentative of that subject comprised of voxel volume elementsconstructed in accordance with MR imaging instrument instructions;interfacing the magnetic resonance image with the a priori nodalinformation from the atlas; and aligning the MR scan and/or segmentingthe MR scan to maximize the post probability of its voxel componentsmatching both position in anatomic space and at least one MR tissuecharacteristic of the nodes determined a priori in the atlas.
 15. Themethod of claim 14 in which two MR scans are obtained at differentpoints in time to develop data about changes in anatomic distribution oforgan tissue components andlor changes in their MR propertiesindependent of imaging instrument instructions.
 16. A method of creatingan a priori nodal atlas as a reference for the consistent alignmentand/or segmentation of subsequent MR scans comprising: assigning to eachnode of the atlas information about location in three dimensionalanatomic space, as well as information about magnetic resonanceproperties of each location in anatomic space for specific tissue typesand prior probability for specific tissue types and prior probability ofspecific tissue types of neighboring nodes for each tissue type at thenodal location.
 17. A method of using an a priori nodal atlas as areference for alignment and/or segmentation of subsequent MR scanscomprising: reading data from an MR scan; reading an a priori nodalatlas wherein, each node of the atlas is based on at least 3 MRI scansand wherein the atlas contains information about location inthree-dimensional anatomic space, as well as information about magneticresonance properties of each location in anatomic space for specifictissue types, prior probability for specific tissue types, and priorprobability for specific tissue types of neighboring nodes for eachtissue type at the nodal location; and aligning and/or segmentingsubsequent scans by operating on voxel based images created inaccordance with instructions of an imaging instrument and applyingtransformations which maximize the probability of matching each voxel toa corresponding node in the atlas.
 18. A system for obtaininginformation about a subject, the system comprising: a processor adaptedto receive information obtained from a magnetic resonance scan and reada prior probability MRI based atlas to determine a tissue type the priorprobability MRI based atlas comprising: a plurality of nodescorresponding to a plurality of voxels representing spatial locations ofa subject, each of said nodes configured to store at least one magneticproperty value as determined by magnetic resonance imaging at least onetissue type prior probability value corresponding to a tissue type of avoxel, thereby determining the tissue type.